Evolution of the semiconductor manufacturing industry is placing ever greater demands on yield management and, in particular, on metrology and inspection systems. Critical dimensions are shrinking while wafer size is increasing. Economics is driving the industry to decrease the time for achieving high-yield, high-value production. Thus, minimizing the total time from detecting a yield problem to fixing it determines the return-on-investment for the semiconductor manufacturer.
For transparent or translucent wafers, an image from certain defect detection systems can contain contributions from both the wafer and the tool parts such as the chuck under the wafer. Defect detection on transparent or translucent wafers provides unique challenges. For example, the chuck under the wafer appears during defect detection of a glass wafer. The glass wafer contains some structures or devices, which can be difficult to discern during defect detection when chuck components are also imaged. When a chuck shows up in the glass wafer image, existing defect detection tools or algorithms cannot meet defect detection sensitivity or throughput targets for semiconductor manufacturers.
In another example, a chuck pattern appears in a bright field image, while chuck surface roughness appears in a dark field image. The chuck patterns in each die are different. Thus, existing defect detection algorithms cannot provide satisfactory defect detection on those transparent or translucent wafers. For example, the algorithm may only be able to detect large defects on wafer with the smallest pixel size (e.g., 10× or 0.65 μm) with degraded inspection sensitivity.
Examples are seen in FIGS. 1-3. FIG. 1 illustrates three exemplary dies on a glass wafer with both bright field and dark field imaging. As seen in the bright field images of die 0 and die 1, the chuck is visible through the glass wafer. Surface roughness can be seen in each of the dark field images. Defects of interest (DOI) are circled in the bright field images of die 0 and die 2. FIG. 2 illustrates image subtraction of die 2 and die 0 and also image subtraction of die 2 and die 1. Noise is present in each image, which makes detection of the defects challenging. FIG. 3 shows additional image analysis of the bright field images. As seen in FIG. 3, there are one or two DOI circled in some die images. The DOI are dwarfed by the chuck components. The algorithm will provide poor defect detection performance of images such as those in FIG. 3.
Therefore, improvements to defect detection on transparent or translucent wafers are needed.